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StructureMC  

Structured Matrix Completion
View on CRAN: Click here


Download and install StructureMC package within the R console
Install from CRAN:
install.packages("StructureMC")

Install from Github:
library("remotes")
install_github("cran/StructureMC")

Install by package version:
library("remotes")
install_version("StructureMC", "1.0")



Attach the package and use:
library("StructureMC")
Maintained by
Yifu Liu
[Scholar Profile | Author Map]
First Published: 2019-09-20
Latest Update: 2019-09-20
Description:
Provides an efficient method to recover the missing block of an approximately low-rank matrix. Current literature on matrix completion focuses primarily on independent sampling models under which the individual observed entries are sampled independently. Motivated by applications in genomic data integration, we propose a new framework of structured matrix completion (SMC) to treat structured missingness by design [Cai T, Cai TT, Zhang A (2016) <doi:10.1080/01621459.2015.1021005>]. Specifically, our proposed method aims at efficient matrix recovery when a subset of the rows and columns of an approximately low-rank matrix are observed. The main function in our package, smc.FUN(), is for recovery of the missing block A22 of an approximately low-rank matrix A given the other blocks A11, A12, A21.
How to cite:
Yifu Liu (2019). StructureMC: Structured Matrix Completion. R package version 1.0, https://cran.r-project.org/web/packages/StructureMC. Accessed 07 Apr. 2025.
Previous versions and publish date:
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Other packages that cited StructureMC R package
View StructureMC citation profile
Other R packages that StructureMC depends, imports, suggests or enhances
Complete documentation for StructureMC
Functions, R codes and Examples using the StructureMC R package
Some associated functions: StructureMC-package . smc.FUN . 
Some associated R codes: StructureMC.R .  Full StructureMC package functions and examples
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